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Evaluating the “geographical awareness” of individuals: an exploratory analysis of Twitter data / Chen Xu in Cartography and Geographic Information Science, vol 40 n° 2 (March 2013)  

Public [article]  inCartography and Geographic Information Science > vol 40 n° 2 (March 2013) . - pp 103 - 115 Titre : Evaluating the “geographical awareness” of individuals: an exploratory analysis of Twitter data Type de document : Article/Communication Auteurs : Chen Xu, Auteur ; David W. Wong, Auteur ; Chaowei Yang, Auteur Année de publication : 2013 Article en page(s) : pp 103 - 115 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatiale [Termes IGN] données issues des réseaux sociaux [Termes IGN] échantillonnage [Termes IGN] exploration de données géographiques [Termes IGN] extraction de données [Termes IGN] fiabilité des données [Termes IGN] géopositionnement [Termes IGN] langage naturel (informatique) [Termes IGN] qualité des données Résumé : (Auteur) A major theme in the geographical studies of social media content such as tweets from Twitter is to extract the locations of content providers (e.g., Twitter users) in order to track their movements or activity patterns. This framework also has been used to detect the dispersion of ideas over space and time. Another theme is to assess how the interaction of these providers may vary between the physical and virtual spaces. However, few geographical studies have explored if social media content can be used to examine the relationship between the characteristics of content providers and their geographical knowledge at different spatial scales. We expected that in general, one's awareness of the local geography should be higher than that of places farther away. In this paper, we explored if such pattern of geographical awareness in the physical space is reflected in the social media content. We reported our detailed examinations of tweets from a set of individuals who have provided substantial information in their profiles. Using text-mining methods, including natural language processing (NLP) techniques, we identified place names mentioned in the tweets and geocoded them. These locations were analyzed in a geographical-hierarchical manner to build a geographical awareness profile for each individual. While these geographical awareness profiles vary quite dramatically, their variations can be explained by the users’ characteristics, which were interpreted from their tweet content. This study demonstrates how social media content may be used to assess the geographical awareness characteristics of a biased sample population. Numéro de notice : A2013-745 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2013.776212 En ligne : https://doi.org/10.1080/15230406.2013.776212 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32881 [article]

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